Update open_australian_legal_embeddings.py

open_australian_legal_embeddings.py

@@ -0,0 +1,109 @@
+# Copyright 2023 Umar Butler.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Open Australian Legal Embeddings: the first open-source embeddings of Australian legislative and judicial documents"""
+
+import datasets
+for module in ('orjson', 'ujson', 'json'):
+    try:
+        json = __import__(module)
+        
+        break
+    except ImportError:
+        pass
+
+_CITATION = """\
+@misc{butler-2023-open-australian-legal-embeddings,
+    author = {Butler, Umar},
+    year = {2023},
+    title = {Open Australian Legal Embeddings},
+    publisher = {Hugging Face},
+    version = {1.0.0},
+    url = {https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings}
+}
+"""
+
+_DESCRIPTION = """\
+The Open Australian Legal Embeddings are the first open-source embeddings of Australian legislative and judicial documents.
+
+Trained on the largest open database of Australian law, the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), the Embeddings consist of roughly 5.2 million 384-dimensional vectors embedded with [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5).
+
+The Embeddings open the door to a wide range of possibilities in the field of Australian legal AI, including the development of document classifiers, search engines and chatbots.
+
+To ensure their accessibility to as wide an audience as possible, the Embeddings are distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md)."""
+
+_HOMEPAGE = "https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings"
+
+_LICENSE = """\
+The Embeddings are distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md)."""
+
+_URLS = {
+    'embeddings' : 'data/embeddings.jsonl',
+    'metadatas' : 'data/metadatas.jsonl',
+    'texts' : 'data/texts.jsonl',
+}
+
+class OpenAustralianLegalEmbeddings(datasets.GeneratorBasedBuilder):
+    """Open Australian Legal Embeddings: the first open-source embeddings of Australian legislative and judicial documents"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    DEFAULT_CONFIG_NAME = "train"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'version_id' : datasets.Value('string'),
+                    'type' : datasets.Value('string'),
+                    'jurisdiction' : datasets.Value('string'),
+                    'source' : datasets.Value('string'),
+                    'citation' : datasets.Value('string'),
+                    'url' : datasets.Value('string'),
+                    'is_last_chunk' : datasets.Value('bool'),
+                    'text' : datasets.Value('string'),
+                    'embedding' : [datasets.Value('float32')]
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'embeddings_path' : downloaded_files['embeddings'],
+                    'metadatas_path' : downloaded_files['metadatas'],
+                    'texts_path' : downloaded_files['texts'],
+                }
+            )
+        ]
+
+    def _generate_examples(self, embeddings_path, metadatas_path, texts_path):
+        with open(embeddings_path, 'rb') as embeddings_file, \
+            open(metadatas_path, 'rb') as metadatas_file, \
+            open(texts_path, 'rb') as texts_file:
+                i = -1
+                
+                for embedding, metadata, text in zip(embeddings_file, metadatas_file, texts_file):
+                    i += 1
+                    yield i, json.loads(metadata) | {
+                        'text' : json.loads(text),
+                        'embedding' : json.loads(embedding)
+                    }